Working papers

• D. Blei and P. Frazier.   Distance dependent Chinese restaurant processes.   October, 2009.   [ArXiv]
• L. Hannah and D. Blei and W. Powell.   Dirichlet process mixtures of generalized linear models.   September, 2009.   [ArXiv]

Publications

• J. Chang and D. Blei.   Hierarchical relational models for document networks.   Annals of Applied Statistics , to appear.   [ArXiv] [Code] [Shorter version from AISTATS 2009]
• D. Blei, T. Griffiths, and M. Jordan.   The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies.   Journal of the ACM, to appear.   [PDF] [Code]
• J. Chang, J. Boyd-Graber, S. Gerris, C. Wang, and D. Blei.   Reading tea leaves: How humans interpret topic models .   Neural Information Processing Systems, 2009.   [PDF]
• C. Wang and D. Blei.   Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process.   Neural Information Processing Systems, 2009.   [PDF] [Supplement]
• C. Wang and D. Blei.   Variational inference for the nested Chinese restaurant process. Neural Information Processing Systems, 2009.   [PDF]
• R. Socher, S. Gershman, A. Perotte, P. Sederberg, D. Blei, and K. Norman. A Bayesian analysis of dynamics in free recall. Neural Information Processing Systems, 2009.   [PDF] [Code and data]
• M. Hoffman, D. Blei, P. Cook.   Finding Latent Sources in Recorded Music With a Shift-Invariant HDP.   International Conference on Digital Audio Effects, 2009.   [PDF]
• J. Boyd-Graber and D. Blei.   Multilingual topic models for unaligned text.   Uncertainty in Artificial Intelligence, 2009.   [PDF]
• J. Chang, J. Boyd-Graber, and D. Blei.   Connections between the lines: Augmenting social networks with text.   Knowledge Discovery and Data Mining, 2009.   [PDF] [Code]
• C. Wang, D. Blei., and L. Fei-Fei.   Simultaneous image classification and annotation.   Computer Vision and Pattern Recognition, 2009.   [PDF] [Code]
• M. Hoffman, P. Cook, and D. Blei.   Bayesian spectral matching: Turning Young MC into MC Hammer via MCMC sampling International Computer Music Conference, 2009.   [PDF]
• C. Wang, B. Thiesson, C. Meek, and D. Blei.   Markov topic models.   Artificial Intelligence and Statistics, 2009.   [PDF]
• M. Hoffman, D. Blei, and P. Cook.   Easy as CBA: A simple probabilistic model for tagging music. International Conference on Music Information Retrieval, 2009.   (Best Student Paper Award)   [PDF]
• D. Blei and J. Lafferty.   Topic Models.   In A. Srivastava and M. Sahami, editors, Text Mining: Theory and Applications. Taylor and Francis, 2009.   [PDF]
• I. Mukherjee and D. Blei.   Relative performance guarantees for approximate inference in latent Dirichlet allocation.   Neural Information Processing Systems, 2008.   [PDF]
• J. Boyd-Graber and D. Blei.   Syntactic topic models.   Neural Information Processing Systems, 2008.   [PDF] [Supplement]
• E. Airoldi, D. Blei, S. Fienberg, and E. Xing.   Mixed membership stochastic blockmodels.   Journal of Machine Learning Research , 9:1981--2014, 2008 .   [PDF][Code] [Shorter version from NIPS 2008]
• M. Hoffman, D. Blei, and P. Cook. Content-based musical similarity computation using the hierarchical Dirichlet process. In International Conference on Music Information Retrieval, 2008. [PDF]
• M. Hoffman, P. Cook, and D. Blei. Data-driven recomposition using the hierarchical Dirichlet process hidden Markov model. In International Computer Music Conference, 2008. [PDF]
• C. Wang, D. Blei, and D. Heckerman. Continuous time dynamic topic models. In Uncertainty in Artificial Intelligence [UAI], 2008. [PDF]
• D. Blei, J. McAuliffe. Supervised topic models. In Advances in Neural Information Processing Systems 21, 2007. [PDF] [digg data] [Code]
• J. Boyd-Graber, D. Blei, and X. Zhu. A topic model for word sense disambiguation. In Empirical Methods in Natural Language Processing, 2007. [PDF]
• W. Li, D. Blei, and A. McCallum. Nonparametric Bayes pachinko allocation. In The 23rd Conference on Uncertainty in Artificial Intelligence, 2007. [PDF]
• D. Kaplan and D. Blei. A computational approach to style in American poetry. In IEEE Conference on Data Mining, 2007.
• D. Blei and J. Lafferty. A correlated topic model of Science. Annals of Applied Statistics. 1:1 17–35. [PDF] [Shorter version from NIPS 18] [Code][Browser]
• M. Dudik, D. Blei, and R. Schapire. Hierarchical maximum entropy density estimation. Proceedings of the 24th International Conference on Machine Learning, 2007. [PDF]
• D. Blei and J. Lafferty. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, 2006. [PDF]
• J. McAuliffe, D. Blei, and M. Jordan. Nonparametric empirical Bayes for the Dirichlet process mixture model. Statistics and Computing, 16[1]:5–14, 2006. [Springer] [TR PDF]
• D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1[1]:121–144, 2006. [A shorter version appeared in ICML 2004]. [PDF]
• Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 2006. 101[476]:1566-1581. [PDF] [Teh's Code]
• T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. In Advances in Neural Information Processing Systems 17, 2005. [PDF]
• D. Blei. Probabilistic Models of Text and Images. PhD thesis, U.C. Berkeley, Division of Computer Science, 2004. [PDF]
• D. Blei and M. Jordan. Modeling annotated data. In Proceedings of the 26th annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 127–134. ACM Press, 2003. [PDF]
• D. Blei, T. Griffiths, M. Jordan, and J. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. In Neural Information Processing Systems [NIPS] 16, 2003. [PDF]
• K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107–1135, 2003. [PDF]
• D. Blei, A. Ng, and M. Jordan. Hierarchical Bayesian models for applications in information retrieval. In J. Bernardo, J. Berger, A. Dawid, D. Heckerman, A. Smith, and M. West, editors, Bayesian Statistics 7, volume 7, pages 25–44. Oxford University Press, 2003.
• D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003. [A shorter version appeared in NIPS 2002]. [PDF] [Code]
• D. Blei, J. Bagnell, and A. McCallum. Learning with scope, with application to information extraction and classification. In Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference [UAI-2002], pages 53–60, San Francisco, CA, 2002. Morgan Kaufmann Publishers.
• D. Blei and P. Moreno. Topic segmentation with an aspect hidden Markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 343–348. ACM Press, 2001. [PDF]
• D. Blei and L. Kaelbling. Shortest paths in a dynamic uncertain domain. In IJCAI Workshop on Adaptive Spatial Representations of Dynamic Environments, 1999. [PDF]